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Internship Graduate Machine Learning Jobs in Illinois

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Internship Graduate Machine Learning information

How much do ML interns get paid?

Machine Learning interns typically earn between $15 and $30 per hour, depending on the company, location, and level of experience. Internships often last 10 to 12 weeks and may include additional benefits such as mentorship and skill development opportunities.

What is the difference between Internship Graduate Machine Learning vs Data Analyst?

AspectInternship Graduate Machine LearningData Analyst
Required CredentialsDegree in Computer Science, Data Science, or related field; basic knowledge of programming and statisticsDegree in Statistics, Mathematics, or related field; proficiency in data visualization and analysis tools
Work EnvironmentTech companies, research labs, startups; project-based, collaborative teamsBusiness, finance, marketing sectors; focus on reporting and data interpretation
Employer & Industry UsageUsed in tech, AI, and research industries for developing machine learning modelsCommon in corporate, finance, and consulting firms for data-driven decision making

While both roles involve working with data, an Internship Graduate Machine Learning focuses on developing algorithms and models using programming skills, often in tech environments. In contrast, a Data Analyst emphasizes interpreting data, creating reports, and supporting business decisions. The roles overlap in data handling but differ in technical depth and application focus.

What are the big 4 internships?

The big 4 internships typically refer to internship programs offered by the four largest professional services firms: Deloitte, PricewaterhouseCoopers (PwC), Ernst & Young (EY), and KPMG. These internships provide opportunities in areas such as audit, consulting, tax, and advisory services, often targeting students pursuing degrees in business, finance, or related fields. They are highly competitive and often include training, mentorship, and potential pathways to full-time employment.

What are the key skills and qualifications needed to thrive as an Internship Graduate in Machine Learning, and why are they important?

To thrive as an Internship Graduate in Machine Learning, you typically need a strong background in mathematics, programming (especially Python), and familiarity with algorithms and data structures, often supported by coursework or a degree in computer science, statistics, or a related field. Hands-on experience with machine learning frameworks like TensorFlow or PyTorch, and knowledge of tools such as Jupyter Notebooks and version control systems like Git, are highly valued. Curiosity, problem-solving, teamwork, and effective communication are crucial soft skills to excel in collaborative and innovative environments. These competencies enable interns to contribute to real-world projects, adapt to fast-changing technologies, and communicate findings clearly within interdisciplinary teams.

What are Internship Graduate Machine Learning positions?

Internship Graduate Machine Learning positions are entry-level roles designed for recent graduates or students who have completed coursework in machine learning, data science, or related fields. These internships provide hands-on experience working with real-world data, building and testing machine learning models, and collaborating with experienced professionals. Interns gain exposure to industry-standard tools and techniques, helping them bridge the gap between academic learning and practical application. Such positions are valuable for building a portfolio, networking, and enhancing job prospects in the rapidly growing field of artificial intelligence.

What types of projects do Internship Graduate Machine Learning roles typically involve, and how are responsibilities structured within the team?

Internship Graduate Machine Learning roles often focus on supporting ongoing research or development projects, such as building predictive models, cleaning and analyzing data, or prototyping algorithms. Interns usually collaborate closely with data scientists and engineers, contributing to specific project milestones while learning best practices in model development and deployment. Responsibilities are often structured to allow for mentorship and feedback, with interns participating in regular team meetings, code reviews, and brainstorming sessions. This collaborative environment provides valuable exposure to real-world machine learning workflows and helps interns build both technical and soft skills relevant to the field.

Which 5 jobs will survive AI?

Jobs that require complex human judgment, creativity, emotional intelligence, and specialized skills—such as healthcare professionals, data scientists, software developers, educators, and skilled tradespeople—are more likely to persist despite AI advancements. These roles often involve tasks that are difficult for AI to fully replicate or automate, especially when combined with continuous learning and adaptability.

Is PG in AI worth it?

A postgraduate degree in AI can enhance qualifications for machine learning internship roles by providing advanced knowledge of algorithms, data analysis, and programming skills. It may improve job prospects and salary potential but is not always mandatory, as practical experience and skills in tools like Python and TensorFlow are highly valued in the field.
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Internship Graduate Machine Learning jobs near you

Machine Learning Engineer

Reyes Holdings | Our Culture

Niles, IL

Full-time

Posted 11 days ago

Job description

Responsibilities

Position Responsibilities: 

  • Design, build, and maintain Continuous Integration/Continuous Development (CI/CD) pipelines for machine learning models 
  • Deploy and manage ML models in production environments using containerization and orchestration technologies 
  • Implement monitoring, logging, and alerting solutions to track model performance, system health, and data drift 
  • Collaborate with data scientists to understand model requirements and optimize the process of transforming models from development to a production-ready state 
  • Create and maintain technical documentation for ML Operations (Ops) processes, infrastructure, and deployments 
  • Define ML/Artificial Intelligence (AI) governance to ensure data security and ethical standards are met for all modeling processes 
  • Travel up to 5% of the time 
  • Other duties as assigned  
Qualifications

Required Education and Experience: 

  • Bachelor's degree in computer science, Data Science, Mathematics, or related quantitative discipline and 3 to 5 plus years of experience in ML Engineering, Software Engineering, or a related field or High School Diploma/General Education Diploma and 7 plus years of the above stated experience 

Preferred Education and Experience: 

  • Master's Degree in computer science, Data Science, or other graduate education in related quantitative fields 
  • Hands-on experience with CI/CD pipelines, automation tools, and version control systems like Azure DevOps, Github, or similar and strong understanding of machine learning concepts and the ML development lifecycle 
  • Experience building ML Ops infrastructure and serving models via cloud platforms such as Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP) 
  • Strong proficiency in Python and working knowledge of Bash/shell scripting for automation and system operations 
  • Strong understanding of Structured Query Language (SQL) and experience with big data platforms, i.e., Snowflake, Databricks, or similar 
  • Experience with Infrastructure-as-Code tools, i.e., Terraform, Azure Resource Manager, or similar
Essential Information for Our EmployeesAt the Reyes Family of Businesses, our Total Rewards Strategy prioritizes the holistic well-being of our employees, and our compensation philosophy embraces diverse factors for fair pay decisions, valuing skills, experience, and the needs of our business. Company policy prohibits discrimination and harassment against any applicant or employee based on any status or basis protected by applicable law. In addition, the Company is committed to providing reasonable accommodation to applicants and employees in accordance with applicable law. Please note, if you are an employee in the US moving from one position to another, you may be subject to additional background screening based on the requirements of the new role.Employment Type: FULL_TIME

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